Index investing has long been popular in developed markets and is now gaining significant traction in developing economies like India, as investors seek cost-effective, diversified investment options. While traditional index funds provide broad market exposure, momentum investing gives an added edge by capitalizing on market trends, offering the potential for enhanced returns. Though momentum strategies are not ideal for risk-averse investors or those with short-term financial needs, they can serve as a high-growth component in a well-diversified portfolio for those comfortable with periodic rebalancing. This study examines the performance of momentum investing compared to traditional market indices, highlighting how momentum indices like Nifty 200 Momentum 30 and Nifty Midcap 150 Momentum 50 consistently outperform benchmarks such as Nifty 50, Nifty Midcap 150, and Nifty 200 over three-year and five-year rolling periods. While momentum investing offers higher returns, it comes with increased volatility, making it more suitable for investors with a long-term horizon and a higher risk appetite. A t-test assuming unequal variances confirms that the superior performance of momentum indices is statistically significant, indicating that the excess returns are not due to chance. Additionally, over a five-year period, momentum indices never recorded negative rolling CAGR, unlike broader indices, reinforcing the importance of staying invested for the long term.

Decision-making has changed a lot in this digital age. This increase in UGC on social media affected tourism because people increasingly relied on social media reviews for travel plans. Today, when planning a trip, most travellers rely upon the opinions and experiences of fellow travellers on the Web. The reason is the large number of UGCs on various social media platforms like Instagram, X, Facebook, and YouTube. Travel blogs, videos, photos, and reviews, among others, are shared by travellers in real-time, and the information can be quickly accessed by other people with internet access. It is important to note that social media reviews greatly influence other people's travel decisions. No matter what age they are, people believe in the opinions of others more than they believe in any form of advertising. In this context, previous studies have found that most travellers like to read reviews before deciding. User-generated content provides authenticity and transparency, which advertisements cannot portray. People can analysed feedback from fellow travellers to decide on their travel plans. Though subjective, other people's opinions and experiences are very informative regarding places, attractions, hotels, and activities one may want to visit or experience. Besides, connecting with other travellers through social media nowadays is very easy, and it helps a person make inquiries and ask for recommendations from real people who have undergone the same experience. Therefore, UGC posts on social media are very crucial in decision-making. Data was collected from 100 youngsters in India and analysed using SPSS software to determine the role of user-generated social media content. Factor analysis results identified 3 factors that influence decisions: Trust and credibility, Risk, and ease of use. The main objective was to identify the types of user-generated content that influence adventure travel decisions among youngsters and to know young traveller’s most commonly used social media platforms for adventure tourism information. 

The rising frequency of business insolvencies underscores the critical need for advanced predictive tools that offer real-time risk assessment. Traditional bankruptcy prediction models, which primarily depend on retrospective financial statement data, often fail to provide timely insights due to reporting delays and data incompleteness. This study proposes a machine learning-based framework for proactive bankruptcy prediction, leveraging ensemble learning techniques for greater accuracy and reliability. The analysis draws on a robust dataset comprising 2,800 firm-year observations from companies listed on the Bombay Stock Exchange (BSE) over the period 2020 to 2025. Quarterly financial indicators and market-based variables were systematically compiled to ensure comprehensive coverage of firm performance. To enhance model performance and mitigate overfitting, three advanced ensemble algorithms—XGBoost, AdaBoost, and Random Forest—were implemented. These models combine bagging and boosting mechanisms to optimize predictive capabilities. Recursive Feature Elimination (RFE) was used to identify the most significant predictors influencing financial distress. The study empirically tested four hypotheses: liquidity constraints as indicators of distress; operational cash flow deficits correlating with default risk; high debt-to-equity ratios signaling instability; and declining return on assets (ROA) as an early warning sign. The results demonstrate that the ensemble-based approach delivers high classification accuracy, with key variables conforming to recognized financial risk indicators. The findings highlight the potential of machine learning models to serve as early detection tools, aiding firms and investors in strategic decision-making. This research emphasizes the transformative role of artificial intelligence in improving financial risk management systems, enabling more dynamic and informed responses to emerging threats of bankruptcy.

The practice of financial organisations implementing socially and environmentally responsible strategies is known as "green banking." It entails offering financial services and products to promote eco-friendly projects. Green banks use energy-efficient operations and reduce paper waste, among other measures, to lessen their environmental effect. By incentivising consumers and businesses to adopt eco-friendly activities, they want to encourage sustainability. Ultimately, green banking unites the banking industry with initiatives to mitigate climate change and save natural resources. (Karthikeyan et al.,2018) Green banking practices are defined as electronic goods and services promoting environmentally friendly activities, such as electronic waste management systems, power consumption reduction, and eco-friendly operations. (Ganesan et al.,2016) Although the banking industry is not thought to be polluting, change is urgently needed. Green banking is a broad approach that integrates environmental factors into every facet of the banking sector. This study has taken Scopus as a citation database to conduct the bibliometric analysis.